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arXiv 2021年

作者： Sauceda, Huziel E. Gálvez-González, Luis E. Chmiela, Stefan Paz-Borbón, Lauro Oliver Müller, Klaus-Robert Tkatchenko, Alexandre Machine Learning Group Technische Universität Berlin Berlin10587 Germany BASLEARN TU Berlin BASF Joint Lab for Machine Learning Technische Universität Berlin Berlin10587 Germany División de Ciencias Exactas y Naturales Universidad de Sonora Blvd. Luis Encinas & Rosales Hermosillo Mexico BIFOLD – Berlin Institute for the Foundations of Learning and Data Germany Instituto de Física Universidad Nacional Autónoma de México Apartado Postal 20-364 CDMX01000 Mexico Google Research Brain team Berlin Germany Department of Artificial Intelligence Korea University Anam-dong Seongbuk-gu Seoul02841 Korea Republic of Max Planck Institute for Informatics Stuhlsatzenhausweg Saarbrücken66123 Germany Department of Physics and Materials Science University of Luxembourg LuxembourgL-1511 Luxembourg

machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often introduce tradeoffs that restrict their practical applicability to small subsets of chemical space or require exhaustive datasets for training. Here, we introduce the Bravais-Inspired Gradient-Domain machine learning (BIGDML) approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 geometries for materials including pristine and defect-containing 2D and 3D semiconductors and metals, as well as chemisorbed and physisorbed atomic and molecular adsorbates on surfaces. The BIGDML model employs the full relevant symmetry group for a given material, does not assume artificial atom types or localization of atomic interactions and exhibits high data efficiency and state-of-the-art energy accuracies (errors substantially below 1 meV per atom) for an extended set of materials. Extensive path-integral molecular dynamics carried out with BIGDML models demonstrate the counterintuitive localization of benzene–graphene dynamics induced by nuclear quantum effects and allow to rationalize the Arrhenius behavior of hydrogen diffusion coefficient in a Pd crystal for a wide range of temperatures. Copyright © 2021, The Authors. All rights reserved.

关键词： machine learning

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Automating airborne pollen classification: Identifying and interpreting hard samples for classifiers

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Heliyon 2025年第2期11卷 e41656页

作者： Milling, Manuel Rampp, Simon D.N. Triantafyllopoulos, Andreas Plaza, Maria P. Brunner, Jens O. Traidl-Hoffmann, Claudia Schuller, Björn W. Damialis, Athanasios CHI – Chair of Health Informatics MRI Technical University of Munich Munich Germany MCML–Munich Center for Machine Learning Germany EIHW – Chair of Embedded Intelligence for Health Care & Wellbeing University of Augsburg Augsburg Germany Institute of Environmental Medicine and Integrative Health Faculty of Medicine University Clinic of Augsburg & University of Augsburg Augsburg Germany Institute of Environmental Medicine Helmholtz Center Munich German Research Center for Environmental Health Germany Faculty of Business and Economics and Faculty of Medicine University of Augsburg Augsburg Germany Department of Technology Management and Economics Technical University of Denmark Denmark Next Generation Technology Region Zealand Denmark Christine Kühne Center for Allergy Research and Education Davos Switzerland MDSI–Munich Data Science Institute Germany GLAM–the Group on Language Audio & Music Imperial College London London United Kingdom Terrestrial Ecology and Climate Change Department of Ecology School of Biology Faculty of Sciences Aristotle University of Thessaloniki Thessaloniki Greece

Deep-learning-based classification of pollen grains has been a major driver towards automatic monitoring of airborne pollen. Yet, despite an abundance of available datasets, little effort has been spent to investigate which aspects pose the biggest challenges to the (often black-box- resembling) pollen classification approaches. To shed some light on this issue, we conducted a sample-level difficulty analysis based on the likelihood for one of the largest automatically-generated datasets of pollen grains on microscopy images and investigated the reason for which certain airborne samples and specific pollen taxa pose particular problems to deep learning algorithms. It is here concluded that the main challenges lie in A) the (partly) co-occurring of multiple pollen grains in a single image, B) the occlusion of specific markers through the 2D capturing of microscopy images, and C) for some taxa, a general lack of salient, unique features. Our code is publicly available under https://***/millinma/SDPollen © 2025

关键词： Deep learning Pollen recognition Sample difficulty analysis

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Lessons Learned from Assessing Trustworthy AI in Practice

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Digital Society 2023年第3期2卷 1-25页

作者： Vetter, Dennis Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hickman, Eleanore Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Wiinblad Mathez, Emilie Tithi, Jesmin Jahan Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Computational Vision and Artificial Intelligence Lab Goethe University Frankfurt Frankfurt Am Main Germany Z-Inspection® Initiative Venice Italy Health Ethics and Policy Lab ETH Zurich Zurich Switzerland Strategy and Innovation Careum Foundation Zurich Switzerland Philosophie Departement Collège André-Laurendeau Montréal Canada École Des Médias Université du Québec À Montréal Montréal Canada Department of Medicine Division of Infectious Diseases and Immunology New York University Grossman School of Medicine New York City USA Department of Computer Science University of Copenhagen Copenhagen Denmark Digital Life Initiative Cornell Tech New York City USA Cluster of Excellence “Machine Learning: New Perspectives for Science” University of Tuebingen Tuebingen Germany School of Law University of Bristol Bristol UK Center for the Study of Ethics in the Professions Illinois Institute of Technology Chicago USA Department of Business Management and Analytics Arcada University of Applied Sciences Helsinki Finland Department of Food & Resource Economics University of Copenhagen Copenhagen Denmark Department of Physiology Faculty of Medicine University of Iceland Reykjavik Iceland QUEST Centre for Responsible Research Berlin Institute of Health Charité Universitätsmedizin Berlin Berlin Germany Faculty of Computing Engineering and the Built Environment School of Computing and Digital Technology Birmingham City University Birmingham UK Parallel Computing Labs Intel Santa Clara USA School of Economics Innovation and Technology Kristiania University College Oslo Norway Data Science Graduate School Seoul National University Seoul South Korea

Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these guidelines focus on high-level and abstract requirements for AI systems, and it is often very difficult to assess if a specific system fulfills these requirements. The Z-Inspection® process provides a holistic and dynamic framework to evaluate the trustworthiness of specific AI systems at different stages of the AI lifecycle, including intended use, design, and development. It focuses, in particular, on the discussion and identification of ethical issues and tensions through the analysis of socio-technical scenarios and a requirement-based framework for ethical and trustworthy AI. This article is a methodological reflection on the Z-Inspection® process. We illustrate how high-level guidelines for ethical and trustworthy AI can be applied in practice and provide insights for both AI researchers and AI practitioners. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of real-world AI systems, as well as key recommendations and practical suggestions on how to ensure a rigorous trustworthiness assessment throughout the lifecycle of an AI system. The results presented in this article are based on our assessments of AI systems in the healthcare sector and environmental monitoring, where we used the framework for trustworthy AI proposed in the Ethics Guidelines for Trustworthy AI by the European Commission’s High-Level Expert Group on AI. However, the assessment process and the lessons learned can be adapted to other domains and include additional frameworks.

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Harnessing multimodal approaches for depression detection using large language models and facial expressions

Npj mental health research

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Npj mental health research 2024年第1期3卷 66页

作者： Misha Sadeghi Robert Richer Bernhard Egger Lena Schindler-Gmelch Lydia Helene Rupp Farnaz Rahimi Matthias Berking Bjoern M Eskofier Machine Learning and Data Analytics Lab (MaD Lab) Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany. misha.sadeghi@fau.de. Machine Learning and Data Analytics Lab (MaD Lab) Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany. Chair of Visual Computing (LGDV) Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91058 Germany. Chair of Clinical Psychology and Psychotherapy (KliPs) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany. Translational Digital Health Group Institute of AI for Health Helmholtz Zentrum München - German Research Center for Environmental Health Neuherberg 85764 Germany.

Detecting depression is a critical component of mental health diagnosis, and accurate assessment is essential for effective treatment. This study introduces a novel, fully automated approach to predicting depression severity using the E-DAIC dataset. We employ Large Language Models (LLMs) to extract depression-related indicators from interview transcripts, utilizing the Patient Health Questionnaire-8 (PHQ-8) score to train the prediction model. Additionally, facial data extracted from video frames is integrated with textual data to create a multimodal model for depression severity prediction. We evaluate three approaches: text-based features, facial features, and a combination of both. Our findings show the best results are achieved by enhancing text data with speech quality assessment, with a mean absolute error of 2.85 and root mean square error of 4.02. This study underscores the potential of automated depression detection, showing text-only models as robust and effective while paving the way for multimodal analysis.

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Ranking-based convolutional neural network models for peptide-MHC binding prediction

arXiv

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arXiv 2020年

作者： Chen, Ziqi Min, Martin Renqiang Ning, Xia Computer Science and Engineering Department Ohio State University ColumbusOH United States Machine Learning Department NEC Labs America PrincetonNJ United States Biomedical Informatics Department Ohio State University ColumbusOH United States Translational Data Analytics Institute Ohio State University ColumbusOH United States

T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identifying peptides that can bind to MHC class-I molecules plays a vital role in the design of peptide vaccines. Many computational methods, for example, the state-of-the-art allele-specific method MHCflurry, have been developed to predict the binding affinities between peptides and MHC molecules. In this manuscript, we develop two allele-specific Convolutional Neural Network (CNN)based methods named ConvM and SpConvM to tackle the binding prediction problem. Specifically, we formulate the problem as to optimize the rankings of peptide-MHC bindings via ranking-based learning objectives. Such optimization is more robust and tolerant to the measurement inaccuracy of binding affinities, and therefore enables more accurate prioritization of binding peptides. In addition, we develop a new position encoding method in ConvM and SpConvM to better identify the most important amino acids for the binding events. We conduct a comprehensive set of experiments using the latest Immune Epitope database (IEDB) datasets. Our experimental results demonstrate that our models significantly outperform the state-of-the-art methods including MHCflurry with an average percentage improvement of 6.70% on AUC and 17.10% on ROC5 across 128 alleles. © 2020, CC BY.

关键词： Peptides

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Explaining bayesian neural networks

arXiv

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arXiv 2021年

作者： Bykov, Kirill Höhne, Marina M.-C. Creosteanu, Adelaida Müller, Klaus-Robert Machine Learning Group Technische Universität Berlin Marchstr. 23 Berlin10587 Germany Department of Artificial Intelligence Korea University Anam-dong Seongbuk-gu Seoul02841 Korea Republic of Max Planck Institute for Informatics Stuhlsatzenhausweg 4 Saarbrücken66123 Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Technische Universität Berlin Berlin Germany Google Research Brain team Berlin Germany Department of Computer Science TU Kaiserslautern Germany Heidelberg Germany Institute of Pathology Charite – Universitätsmedizin Berlin Berlin Germany Aignostics Berlin Germany RIKEN AIP 1-4-1 Nihonbashi Chuo-ku Tokyo Japan

—To make advanced learning machines such as Deep Neural Networks (DNNs) more transparent in decision making, explainable AI (XAI) aims to provide interpretations of DNNs’ predictions. These interpretations are usually given in the form of heatmaps, each one illustrating relevant patterns regarding the prediction for a given instance. Bayesian approaches such as Bayesian Neural Networks (BNNs) so far have a limited form of transparency (model transparency) already built-in through their prior weight distribution, but notably, they lack explanations of their predictions for given instances. In this work, we bring together these two perspectives of transparency into a holistic explanation framework for explaining BNNs. Within the Bayesian framework, the network weights follow a probability distribution. Hence, the standard (deterministic) prediction strategy of DNNs extends in BNNs to a predictive distribution, and thus the standard explanation extends to an explanation distribution. Exploiting this view, we uncover that BNNs implicitly employ multiple heterogeneous prediction strategies. While some of these are inherited from standard DNNs, others are revealed to us by considering the inherent uncertainty in BNNs. Our quantitative and qualitative experiments on toy/benchmark data and real-world data from pathology show that the proposed approach of explaining BNNs can lead to more effective and insightful explanations. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Trend filtering - II. Denoising astronomical signals with varying degrees of smoothness

arXiv

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arXiv 2020年

作者： Politsch, Collin A. Cisewski-Kehe, Jessi Croft, Rupert A.C. Wasserman, Larry Department of Statistics & Data Science Carnegie Mellon University PittsburghPA15213 United States Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States McWilliams Center for Cosmology Carnegie Mellon University PittsburghPA15213 United States Department of Statistics and Data Science Yale University New HavenCT06520 United States Department of Physics Carnegie Mellon University PittsburghPA15213 United States School of Physics University of Melbourne VIC3010 Australia

Trend filtering-first introduced into the astronomical literature in Paper I of this series-is a state-of-the-art statistical tool for denoising one-dimensional signals that possess varying degrees of smoothness. In this work, we demonstrate the broad utility of trend filtering to observational astronomy by discussing how it can contribute to a variety of spectroscopic and time-domain studies. The observations we discuss are (1) the Lyman-α forest of quasar spectra;(2) more general spectroscopy of quasars, galaxies, and stars;(3) stellar light curves with planetary transits;(4) eclipsing binary light curves;and (5) supernova light curves. We study the Lyman-α forest in the greatest detail-using trend filtering to map the large-scale structure of the intergalactic medium along quasar-observer lines of sight. The remaining studies share broad themes of: (1) estimating observable parameters of light curves and spectra;and (2) constructing observational spectral/light-curve templates. We also briefly discuss the utility of trend filtering as a tool for one-dimensional data reduction and compression. Copyright © 2020, The Authors. All rights reserved.

关键词： Galaxies

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Synchronized sensor insoles for clinical gait analysis in home-monitoring applications

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Current Directions in Biomedical Engineering 2018年第1期4卷 433-437页

作者： Roth, Nils Martindale, Christine F. Gaßner, Heiko Kohl, Zacharias Klucken, Jochen Eskofer, Bjoern M. Machine Learning and Data Analytics Lab. Department of Computer Science Friedrich-Alexander-University Erlangen-Nürnberg (FAU) Erlangen Germany Department of Molecular Neurology University Hospital Erlangen Germany

Wearable sensor systems are of increasing interest in clinical gait analysis. However, little information about gait dynamics of patients under free living conditions is available, due to the challenges of integrating such systems unobtrusively into a patient's everyday live. To address this limitation, new, fully integrated low power sensor insoles are proposed, to target applications particularly in home-monitoring scenarios. The insoles combine inertial as well as pressure sensors and feature wireless synchronization to acquire biomechanical data of both feet with a mean timing offset of 15.0 μs. The proposed system was evaluated on 15 patients with mild to severe gait disorders against the GAITRite® system as reference. Gait events based on the insoles' pressure sensors were manually extracted to calculate temporal gait features such as double support time and double support. Compared to the reference system a mean error of 0.06 s ± 0.06 s and 3.89 % ± 2.61 % was achieved, respectively. The proposed insoles proved their ability to acquire synchronized gait parameters and address the requirements for home-monitoring scenarios, pushing the boundaries of clinical gait analysis. © 2018 Nils Roth et al.

关键词： Synchronization

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Predictive inference with the jackknife+

arXiv

引用

arXiv 2019年

作者： Barber, Rina Foygel Candés, Emmanuel J. Ramdas, Aaditya Tibshirani, Ryan J. Department of Statistics University of Chicago Departments of Statistics and Mathematics Stanford University Department of Statistics and Data Science Carnegie Mellon University Machine Learning Department Carnegie Mellon University

This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals. Whereas the jackknife outputs an interval centered at the predicted response of a test point, with the width of the interval determined by the quantiles of leave-one-out residuals, the jack-knife+ also uses the leave-one-out predictions at the test point to account for the variability in the fitted regression function. Assuming exchangeable training samples, we prove that this crucial modification permits rigorous coverage guarantees regardless of the distribution of the data points, for any algorithm that treats the training points symmetrically. Such guarantees are not possible for the original jackknife and we demonstrate examples where the coverage rate may actually vanish. Our theoretical and empirical analysis reveals that the jackknife and the jackknife+ intervals achieve nearly exact coverage and have similar lengths whenever the fitting algorithm obeys some form of stability. Further, we extend the jackknife+ to K-fold cross validation and similarly establish rigorous coverage properties. Our methods are related to cross-conformal prediction proposed by Vovk [2015] and we discuss connections. Copyright © 2019, The Authors. All rights reserved.

关键词： machine learning

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The Omniglot challenge: A 3-year progress report

arXiv

引用

arXiv 2019年

作者： Lake, Brenden M. Salakhutdinov, Ruslan Tenenbaum, Joshua B. Department of Psychology Center for Data Science New York University Machine Learning Department Carnegie Mellon University Department of Brain and Cognitive Sciences Center for Brains Minds and Machines MIT

Three years ago, we released the Omniglot dataset for one-shot learning, along with five challenge tasks and a computational model that addresses these tasks. The model was not meant to be the final word on Omniglot;we hoped that the community would build on our work and develop new approaches. In the time since, we have been pleased to see wide adoption of the dataset. There has been notable progress on one-shot classification, but researchers have adopted new splits and procedures that make the task easier. There has been less progress on the other four tasks. We conclude that recent approaches are still far from human-like concept learning on Omniglot, a challenge that requires performing many tasks with a single model. Copyright © 2019, The Authors. All rights reserved.

关键词： machine learning

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